Differentiation of bipolar disorder versus borderline personality disorder: A machine learning approach

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05.

Comorbid bipolar disorder and borderline personality disorder: Diagnosis using machine learning

Comorbid bipolar disorder (BP) and borderline personality disorder (BPD) presents a diagnostic challenge in its differentiation from each condition individually. We aimed to use a machine learning (ML) approach to differentiate comorbid BP/BPD from both BP and BPD. Participants were assigned DSM diagnoses and compared on self-report measures examining personality, emotion regulation strategies and perceived parental experiences during childhood. 82 participants were assigned as BP, 52 as BPD and 53 as comorbid BP/BPD. ML-derived diagnoses had an accuracy of 79.6% in classifying BP/BPD vs. BP, and 61.7% in classifying BP/BPD vs. BPD. Stress-related paranoid ideation and other core borderline personality items were important in distinguishing BP/BPD vs. BP, whereas deficits in emotion regulation strategies were important in distinguishing BP/BPD vs. BPD. Impulsivity and anger were important across both analyses. We identified clinical variables more distinctive in comorbid BP/BPD, with superior accuracy in distinguishing from BP, and with lower accuracy compared to BPD alone. Such an additive model should assist in sharpening clinical decision making, with future machine learning examination of larger datasets likely to further improve diagnostic accuracy.